Computational Intelligence - November 2013 - 30

1) A natural language based organization of events and relations in the physical and continuous engines is implemented. The capabilities available to the agent are specified fully
using a natural language, enabling a natural "semantic
bridge" between agent intentions and system actions. This
feature enables the world modeling operations and capabilities of the agent to be described in a natural language document that itself compiles into code, whilst also capable of
being read by human operators. Consequently, heterogeneous knowledge of procedures and world models with
the agent and human operators is made possible.
2) The organization of all procedural code is based upon
natural language sentence structures, whilst differing from
the strict object oriented principles followed within
CLARAty, enables code reuse through common operational abstractions.
3) The BDI agent language responsible for rational actions, a
customized variant of the Gwendolen programming language, may be verified by model checking.
4) Three component engines, a physical engine, continuous
engine and reasoning engine, are integrated via an abstraction layer. The slow deliberative and fast reactive responses
are naturally blended together and are not organized into
layers. Timing is determined by the availability of abstract
data completed by the physical and continuous engines and
ready for use by the reasoning engine.
5) Rationality is based on the BDI deliberative agent paradigm, as opposed to real-time planning.
Points 1 and 2 link the two core desires of MDS and CLARAty
by providing a linked and transparent abstraction between high
level system operation and low level function using linguistics,
whilst enabling code reuse via common operational abstractions.

sEnglish sEnglish-system English-is a controlled natural language, i.e. a subset of standard English with meanings of sentences ultimately defined by code in a high level programming
language such as MATLAB, Java or C++ [12]. It is natural language programming made into an exact process. A correctly
formulated sEnglish text compiles into executable program
code unambiguously if predefined sentence structures
together with an ontology are defined. Errors in functionality
are reduced due to inherent verification mechanism of sEnglish upon build. This enables a programmer to enjoy the convenience of natural language while keeping with the usual
determinism of digital programs. Once a database of sentences and ontologies have been generated, the clarity and
configurability of a project written in sEnglish becomes evident. Of particular interest, when applied to development of
an agent system, is the link between the abstract manner in
which sEnglish solutions are developed and the abstractions
of a real agent system. This enables shared 'understanding'
between the computational system and its operator.

IEEE ComputatIonal IntEllIgEnCE magazInE | novEmbEr 2013

Furthermore, the same natural language abstractions can be utilized by the deliberative agent for reasoning.
A. Natural Language Programming of Agent Actions

The capabilities available to an agent implementing the presented architecture are contained within the P and X engines;
these actions, either computational data manipulation or complex interaction with hardware devices, are developed in a natural language facilitated by the sEnglish publisher, an Eclipse
based design environment [41] (break-out box sEnglish).
Just as the agent programming language used within the
Rational Engine encourages an engineer to express decisions
in terms of the beliefs available to an agent, abstracting agent
capabilities in a natural language encourages the development
engineer to encode specific skills in abstractions that are then
shared between operating personnel and the agent. The capabilities abstracted may be divided into P and X abilities. P
abilities, or skills, relate to specific physical actions the agent
may invoke on the world environment; X skills are those
related to complex queries that may be used to assist rational
decision making occurring within R or concerning specific
P skills. It is this shared understanding generated by the
abstraction of agent capabilities that enables coherent development of all rational processes that are linked to agent actions.
Development using sEnglish commences by defining a central ontology, O, to define the concepts pertinent to the target
system and that will be used within a natural language program
document (NLPdocument) P = {O, S, N, m} where S is set
of sentences in a natural language N, N is and underlying programming language such as MATLAB and m is a meaning
definition function that assigns code in N to sentences in S .
An NLP text, T, may be formed through composition of sentences from S and can be denoted by S *. Abstraction of a specific procedural action is performed through an expanding tree
of sentences, whereby the trunk represents a core abstract
action and a leaf represents a trivial component computation
expressed in a target code language N. In practice the Eclipse
plugin of sEnglish Publisher facilitates the mapping of S to code
in N [41, 42]. This methodology provides a natural abstraction
link between the high level meaning of a particular action,
which is the handle used by rational actions, and the low level
specifics of the action performative itself, which operates on
real-time hardware.
B. Rational Agent Decisions via Gwendolen

Rational decision making is based on symbolic reasoning using
plans that are implemented in the Rational Engine, R, and
described with a specialised rational agent (BDI) language
based upon the Gwendolen programming language [43].
Gwendolen is implemented in the Agent Infrastructure Layer
(AIL), a collection of Java classes intended for use in model
checking agent programs.
A full operational semantics for Gwendolen is presented in
[43]. Key components of a Gwendolen agent are a set, R, of
beliefs which are ground first order formulae and a set, I, of

Table of Contents for the Digital Edition of Computational Intelligence - November 2013